In [1]:
# INSTALL REQUIRED LIBRARIES
!pip install yfinance
!pip install beautifulsoup4
!pip install lxml
!pip install plotly
Requirement already satisfied: yfinance in c:\users\sanskriti\anaconda3\lib\site-packages (0.2.65)
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[notice] A new release of pip is available: 24.3.1 -> 25.1.1
[notice] To update, run: python.exe -m pip install --upgrade pip
Requirement already satisfied: beautifulsoup4 in c:\users\sanskriti\anaconda3\lib\site-packages (4.12.3)
Requirement already satisfied: soupsieve>1.2 in c:\users\sanskriti\anaconda3\lib\site-packages (from beautifulsoup4) (2.5)
[notice] A new release of pip is available: 24.3.1 -> 25.1.1
[notice] To update, run: python.exe -m pip install --upgrade pip
Requirement already satisfied: lxml in c:\users\sanskriti\anaconda3\lib\site-packages (5.2.1)
[notice] A new release of pip is available: 24.3.1 -> 25.1.1
[notice] To update, run: python.exe -m pip install --upgrade pip
Requirement already satisfied: plotly in c:\users\sanskriti\anaconda3\lib\site-packages (5.22.0)
Requirement already satisfied: tenacity>=6.2.0 in c:\users\sanskriti\anaconda3\lib\site-packages (from plotly) (8.2.2)
Requirement already satisfied: packaging in c:\users\sanskriti\anaconda3\lib\site-packages (from plotly) (23.2)
[notice] A new release of pip is available: 24.3.1 -> 25.1.1
[notice] To update, run: python.exe -m pip install --upgrade pip
In [3]:
# IMPORTS
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
In [5]:
tesla = yf.Ticker("TSLA")
tesla_data = tesla.history(period="max")
tesla_data.reset_index(inplace=True)
tesla_data.head()
Out[5]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 281494500 0.0 0.0
1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 257806500 0.0 0.0
2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 123282000 0.0 0.0
3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 77097000 0.0 0.0
4 2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 103003500 0.0 0.0
In [7]:
url = "https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue"
headers = {"User-Agent": "Mozilla/5.0"}
html_data = requests.get(url, headers=headers).text
soup = BeautifulSoup(html_data, "html.parser")
tables = soup.find_all("table")

for table in tables:
    if "Tesla Quarterly Revenue" in str(table):
        tesla_revenue = pd.read_html(str(table))[0]
        break

tesla_revenue.columns = ["Date", "Revenue"]
tesla_revenue["Revenue"] = tesla_revenue["Revenue"].replace(r"[\$,]", "", regex=True)
tesla_revenue.dropna(inplace=True)
tesla_revenue.tail()
C:\Users\Sanskriti\AppData\Local\Temp\ipykernel_2288\2438719197.py:9: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.
  tesla_revenue = pd.read_html(str(table))[0]
Out[7]:
Date Revenue
58 2010-09-30 31
59 2010-06-30 28
60 2010-03-31 21
62 2009-09-30 46
63 2009-06-30 27
In [9]:
gamestop = yf.Ticker("GME")
gme_data = gamestop.history(period="max")
gme_data.reset_index(inplace=True)
gme_data.head()
Out[9]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 00:00:00-05:00 1.620129 1.693350 1.603296 1.691667 76216000 0.0 0.0
1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683250 11021600 0.0 0.0
2 2002-02-15 00:00:00-05:00 1.683250 1.687458 1.658001 1.674834 8389600 0.0 0.0
3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578048 1.607505 7410400 0.0 0.0
4 2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0
In [11]:
url = "https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue"
headers = {"User-Agent": "Mozilla/5.0"}
html_data = requests.get(url, headers=headers).text
soup = BeautifulSoup(html_data, "html.parser")
tables = soup.find_all("table")

for table in tables:
    if "GameStop Quarterly Revenue" in str(table):
        gme_revenue = pd.read_html(str(table))[0]
        break

gme_revenue.columns = ["Date", "Revenue"]
gme_revenue["Revenue"] = gme_revenue["Revenue"].replace(r"[\$,]", "", regex=True)
gme_revenue.dropna(inplace=True)
gme_revenue.tail()
C:\Users\Sanskriti\AppData\Local\Temp\ipykernel_2288\2552504574.py:9: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.
  gme_revenue = pd.read_html(str(table))[0]
Out[11]:
Date Revenue
61 2010-01-31 3524
62 2009-10-31 1835
63 2009-07-31 1739
64 2009-04-30 1981
65 2009-01-31 3492
In [13]:
fig = go.Figure()
fig.add_trace(go.Scatter(x=tesla_data['Date'], y=tesla_data['Close'], name='Stock Price'))
fig.update_layout(title='Tesla Stock Price Over Time', xaxis_title='Date', yaxis_title='Price (USD)')
fig.show()
In [17]:
fig = go.Figure()
fig.add_trace(go.Scatter(x=gme_data['Date'], y=gme_data['Close'], name='Stock Price'))
fig.update_layout(title='GameStop Stock Price Over Time', xaxis_title='Date', yaxis_title='Price (USD)')
fig.show()
In [19]:
fig = go.Figure()
fig.add_trace(go.Scatter(x=tesla_data['Date'], y=tesla_data['Close'], name='Stock Price'))
fig.add_trace(go.Scatter(x=tesla_revenue['Date'], y=tesla_revenue['Revenue'].astype(float), name='Revenue'))
fig.update_layout(title='Tesla: Stock Price vs Revenue', xaxis_title='Date', yaxis_title='USD')
fig.show()
In [ ]: